Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f0ccde52550>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f0ccdd92630>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, [None, image_height, image_width, image_channels], name='input_real') 
    input_z = tf.placeholder(tf.float32, [None, z_dim], name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """

    images_shape = images.get_shape().as_list()
    
    # opening discriminator variable scope
    with tf.variable_scope('discriminator', reuse=reuse):
        # leaky ReLU param
        alpha = 0.1
        
        # Applying two convolutional layers with batch normalization and activation function leaky ReLU
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        conv1 = tf.maximum(alpha*conv1, conv1)
        # now flowing data has shape batchx14x14x64
        
        conv2 = tf.layers.conv2d(conv1, 128, 5, strides=2, padding='same')
        conv2 = tf.layers.batch_normalization(conv2, training=True)
        conv2 = tf.maximum(alpha*conv2, conv2)
        # flowing data shape batchx7x7x128
        
        conv3 = tf.layers.conv2d(conv2, 256, 5, 2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3, training=True)
        conv3 = tf.maximum(alpha*conv3, conv3)
        
        # reshaping data to perform a fully connected layer
        conv3_shape = conv3.get_shape().as_list()
        reshaped = tf.reshape(conv3, [-1, conv3_shape[1]*conv3_shape[2]*conv3_shape[3]]) # making it batchx8192
        
        # dense layer
        discriminator_logits = tf.layers.dense(reshaped, 1, activation=None)
        discriminator_output = tf.sigmoid(discriminator_logits)
      
    

        return discriminator_output, discriminator_logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # The generator graph will be structured as follows: fully connected, reshape, batch norm, leaky relu; convolution, 
    # batch norm, leaky relu; convolution, batch norm, leaky relu; 

    
    # everything inside generator variable scope so variables within it have their names starting with generator
    with tf.variable_scope('generator', reuse=not is_train):
        # Leaky ReLU param
        alpha = 0.1
        
        # fully connected layer and reshape
        dense = tf.layers.dense(z, 7*7*512)
        dense = tf.reshape(dense, (-1, 7, 7, 512))
        dense = tf.layers.batch_normalization(dense, training=is_train)
        dense = tf.maximum(alpha*dense, dense)
        
        # first deconvolution
        deconv1 = tf.layers.conv2d_transpose(dense, 64, 5, strides=2, padding='same')
        deconv1 = tf.layers.batch_normalization(deconv1, training=is_train)
        deconv1 = tf.maximum(alpha*deconv1, deconv1)
        
        # Second deconvolution
        deconv2 = tf.layers.conv2d_transpose(deconv1, 32, 5, strides=2, padding='same')
        deconv2 = tf.layers.batch_normalization(deconv2, training=is_train)
        deconv2 = tf.maximum(alpha*deconv2, deconv2)
          
        # logits. Flowing data already has height and width of 28
        generator_logits = tf.layers.conv2d_transpose(deconv2, out_channel_dim, 5, strides=1, padding='same')
        
        # output
        generator_output = tf.tanh(generator_logits)
   
        return generator_output

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [13]:
import numpy as np

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """

    # generator output image
    generator_output = generator(input_z, out_channel_dim)
    
    # real discriminator output and logits
    real_discriminator_output, real_discriminator_logits = discriminator(input_real)
    # fake discriminator output and logits
    fake_discriminator_output, fake_discriminator_logits = discriminator(generator_output, reuse=True)
    
    # discriminator real and fake loss
    d_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_discriminator_logits, labels=tf.zeros_like(fake_discriminator_logits)))
    # adding one side smoothing to the real discriminator loss
    d_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real_discriminator_logits, labels=(tf.ones_like(real_discriminator_logits)*np.random.uniform(0.7, 1.0))))
    discriminator_loss = d_fake_loss + d_real_loss
    
    # generator loss
    generator_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_discriminator_logits, labels=tf.ones_like(fake_discriminator_logits)))
    
    
    return discriminator_loss, generator_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [14]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
  
    # trainable variables
    trainables = tf.trainable_variables()
    d_vars = [var for var in trainables if var.name.startswith('discriminator')]
    g_vars = [var for var in trainables if var.name.startswith('generator')]
    
    # generator and discriminator optimizers
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_op = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_op = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    
    return d_train_op, g_train_op


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [15]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [16]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches. From mnist dataset
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    # resetting tf graph
    tf.reset_default_graph()
    
    # placeholders
    input_real, input_z, lr = model_inputs(data_shape[1],
                                           data_shape[2],
                                           data_shape[3],
                                           z_dim)
    
    # discriminator and generator loss
    discriminator_loss, generator_loss = model_loss(input_real,
                                                    input_z,
                                                    data_shape[3])
    
    # training operations
    d_train_op, g_train_op = model_opt(discriminator_loss,
                                       generator_loss,
                                       lr,
                                       beta1)
    
    saver = tf.train.Saver()
    
    sample_every = 100
    print_losses_every = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        iteration = 0
        
        for epoch_i in range(1, epoch_count+1):
            for batch_images in get_batches(batch_size):
                
                # rescaling batch images
                batch_images = batch_images * 2
                
                # getting batch z noise values
                batch_z = np.random.uniform(-1,1,size=(batch_size, z_dim))
                
                # running optimizers
                _ = sess.run(d_train_op, feed_dict={input_real:batch_images, input_z:batch_z, lr:learning_rate})
                _ = sess.run(g_train_op, feed_dict={input_real:batch_images, input_z:batch_z, lr:learning_rate})
                
                
                # print statistics and sample
                if iteration%print_losses_every==0:
                    # getting losses every 100 batches
                    train_loss_d = discriminator_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = generator_loss.eval({input_z: batch_z})
                    print('Epoch: {:>2}, discriminator_loss = {:.4f}, generator_loss = {:.4f}'.format(epoch_i, train_loss_d, train_loss_g))


                if iteration%sample_every==0:
                    # Sampling from generator with show_generator_output function
                    show_generator_output(sess, 50, input_z, data_shape[3], data_image_mode)
                
                iteration+=1   
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch:  1, discriminator_loss = 4.8678, generator_loss = 0.0117
Epoch:  1, discriminator_loss = 0.5966, generator_loss = 2.3344
Epoch:  1, discriminator_loss = 1.6582, generator_loss = 0.4194
Epoch:  1, discriminator_loss = 1.7900, generator_loss = 1.1901
Epoch:  1, discriminator_loss = 1.1353, generator_loss = 0.7292
Epoch:  1, discriminator_loss = 1.0973, generator_loss = 0.7511
Epoch:  1, discriminator_loss = 1.0336, generator_loss = 0.7576
Epoch:  1, discriminator_loss = 1.2030, generator_loss = 0.5439
Epoch:  1, discriminator_loss = 1.0068, generator_loss = 0.7333
Epoch:  1, discriminator_loss = 0.9384, generator_loss = 0.8745
Epoch:  1, discriminator_loss = 0.9996, generator_loss = 1.0152
Epoch:  1, discriminator_loss = 1.3523, generator_loss = 0.4860
Epoch:  1, discriminator_loss = 0.9961, generator_loss = 0.8717
Epoch:  1, discriminator_loss = 0.8930, generator_loss = 0.8487
Epoch:  1, discriminator_loss = 0.7886, generator_loss = 1.3584
Epoch:  1, discriminator_loss = 1.1203, generator_loss = 0.5896
Epoch:  1, discriminator_loss = 1.0417, generator_loss = 0.9659
Epoch:  1, discriminator_loss = 0.8285, generator_loss = 1.5129
Epoch:  1, discriminator_loss = 0.9646, generator_loss = 1.0089
Epoch:  1, discriminator_loss = 1.2519, generator_loss = 0.5201
Epoch:  1, discriminator_loss = 1.0136, generator_loss = 1.2714
Epoch:  1, discriminator_loss = 0.9533, generator_loss = 1.6224
Epoch:  1, discriminator_loss = 1.1091, generator_loss = 0.9144
Epoch:  1, discriminator_loss = 0.9567, generator_loss = 1.0670
Epoch:  1, discriminator_loss = 1.4494, generator_loss = 0.4664
Epoch:  1, discriminator_loss = 0.8390, generator_loss = 1.0483
Epoch:  1, discriminator_loss = 0.9305, generator_loss = 1.6197
Epoch:  1, discriminator_loss = 0.9474, generator_loss = 1.1158
Epoch:  1, discriminator_loss = 1.2075, generator_loss = 0.5242
Epoch:  1, discriminator_loss = 0.8283, generator_loss = 1.3552
Epoch:  1, discriminator_loss = 0.8724, generator_loss = 1.2982
Epoch:  1, discriminator_loss = 0.9011, generator_loss = 1.1446
Epoch:  1, discriminator_loss = 0.8745, generator_loss = 1.0177
Epoch:  1, discriminator_loss = 0.8838, generator_loss = 0.9665
Epoch:  1, discriminator_loss = 0.9993, generator_loss = 0.8423
Epoch:  1, discriminator_loss = 0.7750, generator_loss = 1.1824
Epoch:  1, discriminator_loss = 0.8726, generator_loss = 1.0845
Epoch:  1, discriminator_loss = 1.0122, generator_loss = 0.7495
Epoch:  1, discriminator_loss = 0.8957, generator_loss = 1.2609
Epoch:  1, discriminator_loss = 1.1098, generator_loss = 0.6501
Epoch:  1, discriminator_loss = 0.9011, generator_loss = 1.0554
Epoch:  1, discriminator_loss = 0.7705, generator_loss = 1.1860
Epoch:  1, discriminator_loss = 0.8025, generator_loss = 1.0484
Epoch:  1, discriminator_loss = 1.5970, generator_loss = 0.3650
Epoch:  1, discriminator_loss = 0.8151, generator_loss = 1.1233
Epoch:  1, discriminator_loss = 0.8990, generator_loss = 1.0341
Epoch:  1, discriminator_loss = 0.7880, generator_loss = 1.5768
Epoch:  1, discriminator_loss = 0.8144, generator_loss = 2.0159
Epoch:  1, discriminator_loss = 1.3343, generator_loss = 0.4931
Epoch:  1, discriminator_loss = 1.3398, generator_loss = 0.4693
Epoch:  1, discriminator_loss = 0.8853, generator_loss = 0.9075
Epoch:  1, discriminator_loss = 2.3215, generator_loss = 3.7561
Epoch:  1, discriminator_loss = 0.7101, generator_loss = 1.2548
Epoch:  1, discriminator_loss = 0.8227, generator_loss = 1.0375
Epoch:  1, discriminator_loss = 0.8287, generator_loss = 0.9174
Epoch:  1, discriminator_loss = 0.8298, generator_loss = 1.1036
Epoch:  1, discriminator_loss = 1.0962, generator_loss = 0.6952
Epoch:  1, discriminator_loss = 0.7991, generator_loss = 1.0589
Epoch:  1, discriminator_loss = 1.2465, generator_loss = 3.0082
Epoch:  1, discriminator_loss = 0.6132, generator_loss = 1.5235
Epoch:  1, discriminator_loss = 0.8229, generator_loss = 1.0145
Epoch:  1, discriminator_loss = 0.8494, generator_loss = 0.9188
Epoch:  1, discriminator_loss = 1.0472, generator_loss = 0.7428
Epoch:  1, discriminator_loss = 0.9573, generator_loss = 0.7793
Epoch:  1, discriminator_loss = 0.9561, generator_loss = 0.8290
Epoch:  1, discriminator_loss = 0.7333, generator_loss = 1.1658
Epoch:  1, discriminator_loss = 0.4608, generator_loss = 1.9606
Epoch:  1, discriminator_loss = 0.6935, generator_loss = 1.3261
Epoch:  1, discriminator_loss = 0.7929, generator_loss = 1.0462
Epoch:  1, discriminator_loss = 1.0809, generator_loss = 0.6911
Epoch:  1, discriminator_loss = 0.6791, generator_loss = 1.4470
Epoch:  1, discriminator_loss = 0.8113, generator_loss = 0.9586
Epoch:  1, discriminator_loss = 0.6641, generator_loss = 1.2299
Epoch:  1, discriminator_loss = 0.7656, generator_loss = 1.9830
Epoch:  1, discriminator_loss = 0.7029, generator_loss = 2.5384
Epoch:  2, discriminator_loss = 0.6513, generator_loss = 1.3903
Epoch:  2, discriminator_loss = 0.8516, generator_loss = 1.0401
Epoch:  2, discriminator_loss = 0.7798, generator_loss = 1.0371
Epoch:  2, discriminator_loss = 1.0540, generator_loss = 0.6960
Epoch:  2, discriminator_loss = 0.5904, generator_loss = 1.5509
Epoch:  2, discriminator_loss = 0.8252, generator_loss = 1.0141
Epoch:  2, discriminator_loss = 0.8034, generator_loss = 1.0309
Epoch:  2, discriminator_loss = 0.6761, generator_loss = 1.6221
Epoch:  2, discriminator_loss = 0.8925, generator_loss = 0.8578
Epoch:  2, discriminator_loss = 0.9322, generator_loss = 1.2553
Epoch:  2, discriminator_loss = 0.8797, generator_loss = 0.8076
Epoch:  2, discriminator_loss = 0.6399, generator_loss = 1.1770
Epoch:  2, discriminator_loss = 1.4241, generator_loss = 0.4095
Epoch:  2, discriminator_loss = 0.7365, generator_loss = 1.3427
Epoch:  2, discriminator_loss = 0.5821, generator_loss = 1.7785
Epoch:  2, discriminator_loss = 1.3305, generator_loss = 0.5748
Epoch:  2, discriminator_loss = 1.9593, generator_loss = 0.2323
Epoch:  2, discriminator_loss = 0.8779, generator_loss = 1.1081
Epoch:  2, discriminator_loss = 0.7494, generator_loss = 1.0774
Epoch:  2, discriminator_loss = 0.5119, generator_loss = 1.6574
Epoch:  2, discriminator_loss = 0.9047, generator_loss = 0.8353
Epoch:  2, discriminator_loss = 0.9095, generator_loss = 0.8800
Epoch:  2, discriminator_loss = 0.9199, generator_loss = 2.3896
Epoch:  2, discriminator_loss = 0.9240, generator_loss = 1.0076
Epoch:  2, discriminator_loss = 0.6666, generator_loss = 1.2543
Epoch:  2, discriminator_loss = 0.7090, generator_loss = 1.2191
Epoch:  2, discriminator_loss = 0.4307, generator_loss = 1.8778
Epoch:  2, discriminator_loss = 0.5285, generator_loss = 1.7520
Epoch:  2, discriminator_loss = 0.7398, generator_loss = 1.1557
Epoch:  2, discriminator_loss = 0.7775, generator_loss = 0.9804
Epoch:  2, discriminator_loss = 0.9168, generator_loss = 0.8654
Epoch:  2, discriminator_loss = 0.7528, generator_loss = 1.0018
Epoch:  2, discriminator_loss = 0.4391, generator_loss = 2.0574
Epoch:  2, discriminator_loss = 0.8350, generator_loss = 1.0308
Epoch:  2, discriminator_loss = 2.0535, generator_loss = 0.2710
Epoch:  2, discriminator_loss = 0.5666, generator_loss = 1.4572
Epoch:  2, discriminator_loss = 0.5308, generator_loss = 1.5710
Epoch:  2, discriminator_loss = 0.6016, generator_loss = 2.2721
Epoch:  2, discriminator_loss = 0.9731, generator_loss = 0.8976
Epoch:  2, discriminator_loss = 0.5609, generator_loss = 1.4025
Epoch:  2, discriminator_loss = 0.6135, generator_loss = 1.4026
Epoch:  2, discriminator_loss = 0.4824, generator_loss = 1.9802
Epoch:  2, discriminator_loss = 0.7309, generator_loss = 1.0793
Epoch:  2, discriminator_loss = 1.9062, generator_loss = 0.2982
Epoch:  2, discriminator_loss = 0.4676, generator_loss = 1.9666
Epoch:  2, discriminator_loss = 0.8572, generator_loss = 1.0082
Epoch:  2, discriminator_loss = 0.6857, generator_loss = 2.4459
Epoch:  2, discriminator_loss = 0.4920, generator_loss = 1.7535
Epoch:  2, discriminator_loss = 1.3170, generator_loss = 0.5736
Epoch:  2, discriminator_loss = 0.5380, generator_loss = 1.5287
Epoch:  2, discriminator_loss = 0.6706, generator_loss = 1.1414
Epoch:  2, discriminator_loss = 0.6445, generator_loss = 1.3491

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [17]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch:  1, discriminator_loss = 5.6273, generator_loss = 0.0049
Epoch:  1, discriminator_loss = 0.3872, generator_loss = 2.5865
Epoch:  1, discriminator_loss = 0.9099, generator_loss = 0.9139
Epoch:  1, discriminator_loss = 1.1631, generator_loss = 0.8330
Epoch:  1, discriminator_loss = 0.7171, generator_loss = 3.0874
Epoch:  1, discriminator_loss = 1.0323, generator_loss = 3.2997
Epoch:  1, discriminator_loss = 0.9049, generator_loss = 1.0908
Epoch:  1, discriminator_loss = 0.7865, generator_loss = 1.1884
Epoch:  1, discriminator_loss = 0.6840, generator_loss = 1.1221
Epoch:  1, discriminator_loss = 1.4510, generator_loss = 0.5076
Epoch:  1, discriminator_loss = 1.1177, generator_loss = 0.6627
Epoch:  1, discriminator_loss = 1.2903, generator_loss = 0.9091
Epoch:  1, discriminator_loss = 1.0472, generator_loss = 2.7199
Epoch:  1, discriminator_loss = 0.7460, generator_loss = 1.1710
Epoch:  1, discriminator_loss = 1.1413, generator_loss = 0.6366
Epoch:  1, discriminator_loss = 0.9001, generator_loss = 0.8555
Epoch:  1, discriminator_loss = 0.7621, generator_loss = 1.4170
Epoch:  1, discriminator_loss = 1.2077, generator_loss = 2.6081
Epoch:  1, discriminator_loss = 1.1409, generator_loss = 0.6645
Epoch:  1, discriminator_loss = 2.1392, generator_loss = 0.1783
Epoch:  1, discriminator_loss = 1.1127, generator_loss = 0.6552
Epoch:  1, discriminator_loss = 1.1402, generator_loss = 0.6629
Epoch:  1, discriminator_loss = 1.9981, generator_loss = 0.2033
Epoch:  1, discriminator_loss = 0.8981, generator_loss = 0.8726
Epoch:  1, discriminator_loss = 1.1421, generator_loss = 0.5581
Epoch:  1, discriminator_loss = 1.2792, generator_loss = 1.8400
Epoch:  1, discriminator_loss = 1.1759, generator_loss = 0.6593
Epoch:  1, discriminator_loss = 0.6476, generator_loss = 1.7594
Epoch:  1, discriminator_loss = 1.0881, generator_loss = 0.6236
Epoch:  1, discriminator_loss = 1.1161, generator_loss = 2.2756
Epoch:  1, discriminator_loss = 1.5267, generator_loss = 0.3477
Epoch:  1, discriminator_loss = 0.7636, generator_loss = 1.0065
Epoch:  1, discriminator_loss = 0.8152, generator_loss = 0.9677
Epoch:  1, discriminator_loss = 0.9770, generator_loss = 1.0438
Epoch:  1, discriminator_loss = 0.9737, generator_loss = 1.2398
Epoch:  1, discriminator_loss = 0.8495, generator_loss = 0.8133
Epoch:  1, discriminator_loss = 1.4403, generator_loss = 0.3993
Epoch:  1, discriminator_loss = 0.8328, generator_loss = 1.9900
Epoch:  1, discriminator_loss = 0.7465, generator_loss = 1.7154
Epoch:  1, discriminator_loss = 1.3952, generator_loss = 0.4259
Epoch:  1, discriminator_loss = 0.7594, generator_loss = 1.5991
Epoch:  1, discriminator_loss = 0.7753, generator_loss = 1.0145
Epoch:  1, discriminator_loss = 0.8348, generator_loss = 0.9691
Epoch:  1, discriminator_loss = 0.7110, generator_loss = 1.1509
Epoch:  1, discriminator_loss = 1.4712, generator_loss = 0.3490
Epoch:  1, discriminator_loss = 0.9728, generator_loss = 0.7413
Epoch:  1, discriminator_loss = 0.6004, generator_loss = 1.7302
Epoch:  1, discriminator_loss = 0.8810, generator_loss = 1.0773
Epoch:  1, discriminator_loss = 1.3039, generator_loss = 0.4245
Epoch:  1, discriminator_loss = 1.3717, generator_loss = 0.4365
Epoch:  1, discriminator_loss = 0.7573, generator_loss = 1.6681
Epoch:  1, discriminator_loss = 0.8606, generator_loss = 0.8177
Epoch:  1, discriminator_loss = 1.9974, generator_loss = 0.1984
Epoch:  1, discriminator_loss = 1.1285, generator_loss = 1.2313
Epoch:  1, discriminator_loss = 1.7375, generator_loss = 0.2541
Epoch:  1, discriminator_loss = 0.8377, generator_loss = 1.6400
Epoch:  1, discriminator_loss = 0.7925, generator_loss = 1.4857
Epoch:  1, discriminator_loss = 0.6414, generator_loss = 1.3907
Epoch:  1, discriminator_loss = 1.9107, generator_loss = 0.2247
Epoch:  1, discriminator_loss = 0.7172, generator_loss = 0.9945
Epoch:  1, discriminator_loss = 0.8474, generator_loss = 0.9071
Epoch:  1, discriminator_loss = 1.4131, generator_loss = 0.4269
Epoch:  1, discriminator_loss = 0.5787, generator_loss = 1.5891
Epoch:  1, discriminator_loss = 0.9679, generator_loss = 0.7563
Epoch:  1, discriminator_loss = 1.5483, generator_loss = 0.3513
Epoch:  1, discriminator_loss = 0.6904, generator_loss = 1.1269
Epoch:  1, discriminator_loss = 1.8198, generator_loss = 0.2481
Epoch:  1, discriminator_loss = 0.7702, generator_loss = 1.0011
Epoch:  1, discriminator_loss = 1.1416, generator_loss = 0.7316
Epoch:  1, discriminator_loss = 1.2098, generator_loss = 1.8997
Epoch:  1, discriminator_loss = 0.7200, generator_loss = 1.3854
Epoch:  1, discriminator_loss = 0.9757, generator_loss = 0.8327
Epoch:  1, discriminator_loss = 1.5626, generator_loss = 0.3108
Epoch:  1, discriminator_loss = 1.3002, generator_loss = 0.4457
Epoch:  1, discriminator_loss = 1.1092, generator_loss = 0.6395
Epoch:  1, discriminator_loss = 1.0422, generator_loss = 0.8449
Epoch:  1, discriminator_loss = 0.8053, generator_loss = 0.8965
Epoch:  1, discriminator_loss = 0.7698, generator_loss = 0.9239
Epoch:  1, discriminator_loss = 0.7104, generator_loss = 1.1033
Epoch:  1, discriminator_loss = 1.6020, generator_loss = 0.3099
Epoch:  1, discriminator_loss = 0.2512, generator_loss = 2.6547
Epoch:  1, discriminator_loss = 1.2688, generator_loss = 0.4874
Epoch:  1, discriminator_loss = 0.6077, generator_loss = 1.2994
Epoch:  1, discriminator_loss = 1.0147, generator_loss = 0.6501
Epoch:  1, discriminator_loss = 0.7957, generator_loss = 0.9068
Epoch:  1, discriminator_loss = 0.9122, generator_loss = 0.7220
Epoch:  1, discriminator_loss = 0.3498, generator_loss = 1.7829
Epoch:  1, discriminator_loss = 0.6602, generator_loss = 1.1518
Epoch:  1, discriminator_loss = 1.4412, generator_loss = 0.4886
Epoch:  1, discriminator_loss = 1.9844, generator_loss = 0.1898
Epoch:  1, discriminator_loss = 0.7059, generator_loss = 1.9714
Epoch:  1, discriminator_loss = 0.7783, generator_loss = 1.0386
Epoch:  1, discriminator_loss = 0.9241, generator_loss = 0.7958
Epoch:  1, discriminator_loss = 1.2646, generator_loss = 0.4905
Epoch:  1, discriminator_loss = 0.9773, generator_loss = 0.8455
Epoch:  1, discriminator_loss = 0.7147, generator_loss = 1.0121
Epoch:  1, discriminator_loss = 0.7201, generator_loss = 1.2527
Epoch:  1, discriminator_loss = 1.5409, generator_loss = 0.3330
Epoch:  1, discriminator_loss = 1.4328, generator_loss = 0.4040
Epoch:  1, discriminator_loss = 0.8408, generator_loss = 0.8801
Epoch:  1, discriminator_loss = 1.2260, generator_loss = 0.5144
Epoch:  1, discriminator_loss = 0.5889, generator_loss = 1.7635
Epoch:  1, discriminator_loss = 1.1026, generator_loss = 2.6029
Epoch:  1, discriminator_loss = 0.7038, generator_loss = 1.2120
Epoch:  1, discriminator_loss = 1.3230, generator_loss = 0.4416
Epoch:  1, discriminator_loss = 1.1451, generator_loss = 0.5734
Epoch:  1, discriminator_loss = 0.9643, generator_loss = 0.7662
Epoch:  1, discriminator_loss = 0.5786, generator_loss = 1.5176
Epoch:  1, discriminator_loss = 1.1855, generator_loss = 0.5171
Epoch:  1, discriminator_loss = 1.4762, generator_loss = 0.3955
Epoch:  1, discriminator_loss = 1.2839, generator_loss = 0.4154
Epoch:  1, discriminator_loss = 1.6292, generator_loss = 0.3272
Epoch:  1, discriminator_loss = 1.1761, generator_loss = 0.5885
Epoch:  1, discriminator_loss = 0.5846, generator_loss = 1.6900
Epoch:  1, discriminator_loss = 0.7337, generator_loss = 1.0854
Epoch:  1, discriminator_loss = 0.8045, generator_loss = 1.0474
Epoch:  1, discriminator_loss = 0.7687, generator_loss = 1.2375
Epoch:  1, discriminator_loss = 0.8742, generator_loss = 0.8150
Epoch:  1, discriminator_loss = 0.8358, generator_loss = 1.1490
Epoch:  1, discriminator_loss = 0.7563, generator_loss = 1.3255
Epoch:  1, discriminator_loss = 0.7415, generator_loss = 1.0911
Epoch:  1, discriminator_loss = 1.2536, generator_loss = 0.4857
Epoch:  1, discriminator_loss = 1.1185, generator_loss = 0.6860
Epoch:  1, discriminator_loss = 0.6865, generator_loss = 1.4282
Epoch:  1, discriminator_loss = 0.6334, generator_loss = 1.2222
Epoch:  1, discriminator_loss = 1.0080, generator_loss = 0.7567
Epoch:  1, discriminator_loss = 0.9157, generator_loss = 1.0897
Epoch:  1, discriminator_loss = 0.8358, generator_loss = 1.0730
Epoch:  1, discriminator_loss = 0.9987, generator_loss = 0.6431
Epoch:  1, discriminator_loss = 1.1373, generator_loss = 0.5727
Epoch:  1, discriminator_loss = 0.9780, generator_loss = 1.5052
Epoch:  1, discriminator_loss = 0.7644, generator_loss = 0.9343
Epoch:  1, discriminator_loss = 0.7613, generator_loss = 1.6836
Epoch:  1, discriminator_loss = 0.9952, generator_loss = 0.8972
Epoch:  1, discriminator_loss = 0.8378, generator_loss = 0.9512
Epoch:  1, discriminator_loss = 1.4748, generator_loss = 0.3550
Epoch:  1, discriminator_loss = 0.4101, generator_loss = 1.7714
Epoch:  1, discriminator_loss = 0.8903, generator_loss = 0.7317
Epoch:  1, discriminator_loss = 0.3677, generator_loss = 1.8440
Epoch:  1, discriminator_loss = 1.3981, generator_loss = 0.4102
Epoch:  1, discriminator_loss = 1.5090, generator_loss = 0.3913
Epoch:  1, discriminator_loss = 0.8294, generator_loss = 0.8480
Epoch:  1, discriminator_loss = 0.8431, generator_loss = 1.0535
Epoch:  1, discriminator_loss = 1.2756, generator_loss = 0.5533
Epoch:  1, discriminator_loss = 1.2413, generator_loss = 0.5437
Epoch:  1, discriminator_loss = 0.8920, generator_loss = 0.7508
Epoch:  1, discriminator_loss = 0.7171, generator_loss = 1.4335
Epoch:  1, discriminator_loss = 1.4772, generator_loss = 0.3796
Epoch:  1, discriminator_loss = 0.7882, generator_loss = 1.0120
Epoch:  1, discriminator_loss = 1.1377, generator_loss = 0.6360
Epoch:  1, discriminator_loss = 1.4467, generator_loss = 0.3806
Epoch:  1, discriminator_loss = 0.5479, generator_loss = 1.9806
Epoch:  1, discriminator_loss = 1.2164, generator_loss = 0.5325
Epoch:  1, discriminator_loss = 1.8699, generator_loss = 0.2439
Epoch:  1, discriminator_loss = 1.1701, generator_loss = 0.5621
Epoch:  1, discriminator_loss = 1.1397, generator_loss = 0.5507
Epoch:  1, discriminator_loss = 0.6579, generator_loss = 1.1133
Epoch:  1, discriminator_loss = 2.4351, generator_loss = 0.1434
Epoch:  1, discriminator_loss = 1.3170, generator_loss = 0.4849
Epoch:  1, discriminator_loss = 1.0826, generator_loss = 0.5870
Epoch:  1, discriminator_loss = 0.6480, generator_loss = 1.3352
Epoch:  1, discriminator_loss = 0.6271, generator_loss = 1.2585
Epoch:  1, discriminator_loss = 1.3761, generator_loss = 0.4116
Epoch:  1, discriminator_loss = 1.7903, generator_loss = 0.2686
Epoch:  1, discriminator_loss = 0.9146, generator_loss = 0.8856
Epoch:  1, discriminator_loss = 1.1926, generator_loss = 0.5866
Epoch:  1, discriminator_loss = 0.7673, generator_loss = 0.9026
Epoch:  1, discriminator_loss = 2.6041, generator_loss = 0.1223
Epoch:  1, discriminator_loss = 0.8009, generator_loss = 1.0367
Epoch:  1, discriminator_loss = 0.6827, generator_loss = 1.1498
Epoch:  1, discriminator_loss = 1.3639, generator_loss = 0.4718
Epoch:  1, discriminator_loss = 1.6246, generator_loss = 0.3311
Epoch:  1, discriminator_loss = 0.6887, generator_loss = 1.1691
Epoch:  1, discriminator_loss = 0.8082, generator_loss = 0.8843
Epoch:  1, discriminator_loss = 0.7784, generator_loss = 0.9556
Epoch:  1, discriminator_loss = 0.7301, generator_loss = 2.1530
Epoch:  1, discriminator_loss = 0.3177, generator_loss = 2.0907
Epoch:  1, discriminator_loss = 0.4308, generator_loss = 2.2538
Epoch:  1, discriminator_loss = 0.3224, generator_loss = 2.9818
Epoch:  1, discriminator_loss = 1.2219, generator_loss = 0.5613
Epoch:  1, discriminator_loss = 0.6094, generator_loss = 2.8627
Epoch:  1, discriminator_loss = 0.8302, generator_loss = 2.8319
Epoch:  1, discriminator_loss = 1.1763, generator_loss = 0.6489
Epoch:  1, discriminator_loss = 0.4160, generator_loss = 1.7472
Epoch:  1, discriminator_loss = 1.0522, generator_loss = 0.6697
Epoch:  1, discriminator_loss = 0.4831, generator_loss = 1.5683
Epoch:  1, discriminator_loss = 0.4881, generator_loss = 1.5971
Epoch:  1, discriminator_loss = 1.6317, generator_loss = 0.2996
Epoch:  1, discriminator_loss = 1.3450, generator_loss = 0.4232
Epoch:  1, discriminator_loss = 0.3912, generator_loss = 4.2755
Epoch:  1, discriminator_loss = 0.4334, generator_loss = 1.9401
Epoch:  1, discriminator_loss = 1.9852, generator_loss = 0.2049
Epoch:  1, discriminator_loss = 0.7497, generator_loss = 0.9831
Epoch:  1, discriminator_loss = 1.4374, generator_loss = 0.4319
Epoch:  1, discriminator_loss = 0.9224, generator_loss = 1.5892
Epoch:  1, discriminator_loss = 0.2906, generator_loss = 2.2027
Epoch:  1, discriminator_loss = 0.6536, generator_loss = 1.1853
Epoch:  1, discriminator_loss = 0.4957, generator_loss = 3.0945
Epoch:  1, discriminator_loss = 0.8225, generator_loss = 2.2765
Epoch:  1, discriminator_loss = 0.8251, generator_loss = 1.1042
Epoch:  1, discriminator_loss = 0.3806, generator_loss = 2.1333
Epoch:  1, discriminator_loss = 0.4705, generator_loss = 2.0298
Epoch:  1, discriminator_loss = 0.5524, generator_loss = 1.3164
Epoch:  1, discriminator_loss = 0.8344, generator_loss = 0.7780
Epoch:  1, discriminator_loss = 1.2103, generator_loss = 0.5149
Epoch:  1, discriminator_loss = 0.3968, generator_loss = 2.4222
Epoch:  1, discriminator_loss = 0.7054, generator_loss = 0.9842
Epoch:  1, discriminator_loss = 0.9404, generator_loss = 0.7657
Epoch:  1, discriminator_loss = 0.5409, generator_loss = 1.3585
Epoch:  1, discriminator_loss = 0.3340, generator_loss = 2.0021
Epoch:  1, discriminator_loss = 0.3506, generator_loss = 1.9700
Epoch:  1, discriminator_loss = 0.9171, generator_loss = 0.8541
Epoch:  1, discriminator_loss = 2.1174, generator_loss = 0.1692
Epoch:  1, discriminator_loss = 0.5068, generator_loss = 1.5439
Epoch:  1, discriminator_loss = 0.5820, generator_loss = 1.2115
Epoch:  1, discriminator_loss = 1.4290, generator_loss = 0.4353
Epoch:  1, discriminator_loss = 0.4014, generator_loss = 2.3558
Epoch:  1, discriminator_loss = 0.9844, generator_loss = 0.7112
Epoch:  1, discriminator_loss = 1.2271, generator_loss = 0.5647
Epoch:  1, discriminator_loss = 0.7391, generator_loss = 1.4353
Epoch:  1, discriminator_loss = 0.8186, generator_loss = 1.8056
Epoch:  1, discriminator_loss = 0.4633, generator_loss = 1.6538
Epoch:  1, discriminator_loss = 0.2975, generator_loss = 2.4037
Epoch:  1, discriminator_loss = 0.9990, generator_loss = 0.6435
Epoch:  1, discriminator_loss = 1.0379, generator_loss = 0.6295
Epoch:  1, discriminator_loss = 0.5064, generator_loss = 2.5144
Epoch:  1, discriminator_loss = 0.8516, generator_loss = 1.6328
Epoch:  1, discriminator_loss = 1.1075, generator_loss = 0.7059
Epoch:  1, discriminator_loss = 0.3174, generator_loss = 2.1134
Epoch:  1, discriminator_loss = 0.3090, generator_loss = 2.2138
Epoch:  1, discriminator_loss = 1.3980, generator_loss = 0.4571
Epoch:  1, discriminator_loss = 0.4054, generator_loss = 1.9058
Epoch:  1, discriminator_loss = 0.4073, generator_loss = 1.6176
Epoch:  1, discriminator_loss = 0.3619, generator_loss = 2.2281
Epoch:  1, discriminator_loss = 1.6653, generator_loss = 0.2847
Epoch:  1, discriminator_loss = 0.7295, generator_loss = 0.9784
Epoch:  1, discriminator_loss = 1.0320, generator_loss = 0.6801
Epoch:  1, discriminator_loss = 0.4309, generator_loss = 2.0295
Epoch:  1, discriminator_loss = 0.4785, generator_loss = 1.5694
Epoch:  1, discriminator_loss = 0.8452, generator_loss = 0.8791
Epoch:  1, discriminator_loss = 0.6496, generator_loss = 3.8197
Epoch:  1, discriminator_loss = 1.3499, generator_loss = 0.4284
Epoch:  1, discriminator_loss = 0.4498, generator_loss = 1.4950
Epoch:  1, discriminator_loss = 0.5206, generator_loss = 1.3240
Epoch:  1, discriminator_loss = 1.4058, generator_loss = 0.4008
Epoch:  1, discriminator_loss = 1.6847, generator_loss = 0.3090
Epoch:  1, discriminator_loss = 0.4378, generator_loss = 1.7326
Epoch:  1, discriminator_loss = 1.8859, generator_loss = 0.2302
Epoch:  1, discriminator_loss = 0.6350, generator_loss = 1.6139
Epoch:  1, discriminator_loss = 0.7871, generator_loss = 0.9264
Epoch:  1, discriminator_loss = 0.7866, generator_loss = 1.0439
Epoch:  1, discriminator_loss = 0.7484, generator_loss = 1.2319
Epoch:  1, discriminator_loss = 0.2804, generator_loss = 2.5210
Epoch:  1, discriminator_loss = 0.7036, generator_loss = 1.0145
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-17-558b20587d66> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     64         if type is None:
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:
     68                 return

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3614     finally:
   3615       if self._enforce_nesting:
-> 3616         if self.stack[-1] is not default:
   3617           raise AssertionError(
   3618               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.